{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DeepExplainer Genomics Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This runs DeepExplainer with the model trained on simulated genomic data from the DeepLIFT repo (https://github.com/kundajelab/deeplift/blob/master/examples/genomics/genomics_simulation.ipynb), using a dynamic reference (i.e. the reference varies depending on the input sequence; in this case, the reference is a collection of dinucleotide-shuffled versions of the input sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulated data was as follows:\n",
    "\n",
    "* 1/4 sequences with BOTH 1-3 instances of a GATA_disc1 motif AND 1-3 instances of a TAL1_known1 motif; these were labelled 1,1,1\n",
    "* 1/4 sequences with 1-3 instances of a GATA_disc1 motif embedded; these were labelled 0,1,0\n",
    "* 1/4 sequences with 1-3 instances of a TAL1_known1 motif embedded; these were labelled 0,0,1\n",
    "* 1/4 sequences with no motif; these were labelled 0,0,0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain data and keras model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pull in the relevant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "! [[ ! -f sequences.simdata.gz ]] && wget https://raw.githubusercontent.com/AvantiShri/model_storage/db919b12f750e5844402153233249bb3d24e9e9a/deeplift/genomics/sequences.simdata.gz\n",
    "! [[ ! -f keras2_conv1d_record_5_model_PQzyq_modelJson.json ]] && wget https://raw.githubusercontent.com/AvantiShri/model_storage/b6e1d69/deeplift/genomics/keras2_conv1d_record_5_model_PQzyq_modelJson.json\n",
    "! [[ ! -f keras2_conv1d_record_5_model_PQzyq_modelWeights.h5 ]] && wget https://raw.githubusercontent.com/AvantiShri/model_storage/b6e1d69/deeplift/genomics/keras2_conv1d_record_5_model_PQzyq_modelWeights.h5\n",
    "! [[ ! -f test.txt.gz ]] && wget https://raw.githubusercontent.com/AvantiShri/model_storage/9aadb769735c60eb90f7d3d896632ac749a1bdd2/deeplift/genomics/test.txt.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: simdna in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (0.4.3.2)\n",
      "Requirement already satisfied: numpy>=1.9 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from simdna) (1.26.3)\n",
      "Requirement already satisfied: matplotlib in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from simdna) (3.8.2)\n",
      "Requirement already satisfied: scipy in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from simdna) (1.11.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from matplotlib->simdna) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from matplotlib->simdna) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from matplotlib->simdna) (4.47.2)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from matplotlib->simdna) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from matplotlib->simdna) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from matplotlib->simdna) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from matplotlib->simdna) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from matplotlib->simdna) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib->simdna) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install simdna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "\n",
    "import simdna.synthetic as synthetic\n",
    "\n",
    "data_filename = \"sequences.simdata.gz\"\n",
    "\n",
    "# read in the data in the testing set\n",
    "test_ids_fh = gzip.open(\"test.txt.gz\", \"rb\")\n",
    "ids_to_load = [x.decode(\"utf-8\").rstrip(\"\\n\") for x in test_ids_fh]\n",
    "data = synthetic.read_simdata_file(data_filename, ids_to_load=ids_to_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "# this is set up for 1d convolutions where examples\n",
    "# have dimensions (len, num_channels)\n",
    "# the channel axis is the axis for one-hot encoding.\n",
    "def one_hot_encode_along_channel_axis(sequence):\n",
    "    to_return = np.zeros((len(sequence), 4), dtype=np.int8)\n",
    "    seq_to_one_hot_fill_in_array(zeros_array=to_return, sequence=sequence, one_hot_axis=1)\n",
    "    return to_return\n",
    "\n",
    "\n",
    "def seq_to_one_hot_fill_in_array(zeros_array, sequence, one_hot_axis):\n",
    "    assert one_hot_axis in (0, 1)\n",
    "    if one_hot_axis == 0:\n",
    "        assert zeros_array.shape[1] == len(sequence)\n",
    "    elif one_hot_axis == 1:\n",
    "        assert zeros_array.shape[0] == len(sequence)\n",
    "    # will mutate zeros_array\n",
    "    for i, char in enumerate(sequence):\n",
    "        if char in (\"A\", \"a\"):\n",
    "            char_idx = 0\n",
    "        elif char in (\"C\", \"c\"):\n",
    "            char_idx = 1\n",
    "        elif char in (\"G\", \"g\"):\n",
    "            char_idx = 2\n",
    "        elif char in (\"T\", \"t\"):\n",
    "            char_idx = 3\n",
    "        elif char in (\"N\", \"n\"):\n",
    "            continue  # leave that pos as all 0's\n",
    "        else:\n",
    "            raise RuntimeError(\"Unsupported character: \" + str(char))\n",
    "        if one_hot_axis == 0:\n",
    "            zeros_array[char_idx, i] = 1\n",
    "        elif one_hot_axis == 1:\n",
    "            zeros_array[i, char_idx] = 1\n",
    "\n",
    "\n",
    "onehot_data = np.array([one_hot_encode_along_channel_axis(seq) for seq in data.sequences])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import model_from_json\n",
    "\n",
    "# load the keras model\n",
    "keras_model_weights = \"keras2_conv1d_record_5_model_PQzyq_modelWeights.h5\"\n",
    "keras_model_json = \"keras2_conv1d_record_5_model_PQzyq_modelJson.json\"\n",
    "\n",
    "keras_model = model_from_json(open(keras_model_json).read())\n",
    "keras_model.load_weights(keras_model_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the deeplift package for the dinucleotide shuffling and visualization code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: deeplift in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (0.6.13.0)\n",
      "Requirement already satisfied: numpy>=1.9 in /home/xiao/miniforge3/envs/shap/lib/python3.11/site-packages (from deeplift) (1.26.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install deeplift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute importance scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the function that generates the reference, in this case by performing a dinucleotide shuffle of the given input sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deeplift.dinuc_shuffle import dinuc_shuffle\n",
    "\n",
    "\n",
    "def shuffle_several_times(s):\n",
    "    s = np.squeeze(s)\n",
    "    return dinuc_shuffle(s, num_shufs=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run DeepExplainer with the dynamic reference function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "seqs_to_explain = onehot_data[[0, 3, 9]]  # these three are positive for task 0\n",
    "dinuc_shuff_explainer = shap.DeepExplainer((keras_model.input, keras_model.output[:, 0]), shuffle_several_times)\n",
    "raw_shap_explanations = dinuc_shuff_explainer.shap_values(seqs_to_explain, check_additivity=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize scores on individual sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores for example 0\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores for example 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores for example 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from deeplift.visualization import viz_sequence\n",
    "\n",
    "# project the importance at each position onto the base that's actually present\n",
    "dinuc_shuff_explanations = np.sum(raw_shap_explanations, axis=-1)[:, :, None] * seqs_to_explain\n",
    "for idx, dinuc_shuff_explanation in zip([0, 3, 9], dinuc_shuff_explanations):\n",
    "    print(\"Scores for example\", idx)\n",
    "    highlight = {\n",
    "        \"blue\": [\n",
    "            (embedding.startPos, embedding.startPos + len(embedding.what))\n",
    "            for embedding in data.embeddings[idx]\n",
    "            if \"GATA_disc1\" in embedding.what.getDescription()\n",
    "        ],\n",
    "        \"green\": [\n",
    "            (embedding.startPos, embedding.startPos + len(embedding.what))\n",
    "            for embedding in data.embeddings[idx]\n",
    "            if \"TAL1_known1\" in embedding.what.getDescription()\n",
    "        ],\n",
    "    }\n",
    "    viz_sequence.plot_weights(dinuc_shuff_explanation, subticks_frequency=20, highlight=highlight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots above show the importance scores for three example sequences on the task that predicts sequences containing both GATA_disc1 and TAL1_known1 motifs. Letter height reflects the score. Blue boxes indicate the ground-truth locations of the inserted GATA_disc1 motifs, green boxes indicate the ground-truth locations of the inserted TAL1_known1 motifs."
   ]
  }
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